Identification of novel lipid biomarkers in xmrk- and Myc-induced models of hepatocellular carcinoma in zebrafish

Background Hepatocellular carcinoma (HCC) is the predominant form of liver cancer and is accompanied by complex dysregulation of lipids. Increasing evidence suggests that particular lipid species are associated with HCC progression. Here, we aimed to identify lipid biomarkers of HCC associated with the induction of two oncogenes, xmrk, a zebrafish homolog of the human epidermal growth factor receptor (EGFR), and Myc, a regulator of EGFR expression during HCC. Methods We induced HCC in transgenic xmrk, Myc, and xmrk/Myc zebrafish models. Liver specimens were histologically analyzed to characterize the HCC stage, Oil-Red-O stained to detect lipids, and liquid chromatography/mass spectrometry analyzed to assign and quantify lipid species. Quantitative real-time polymerase chain reaction was used to measure lipid metabolic gene expression in liver samples. Lipid species data was analyzed using univariate and multivariate logistic modeling to correlate lipid class levels with HCC progression. Results We found that induction of xmrk, Myc and xmrk/Myc caused different stages of HCC. Lipid deposition and class levels generally increased during tumor progression, but triglyceride levels decreased. Myc appears to control early HCC stage lipid species levels in double transgenics, whereas xmrk may take over this role in later stages. Lipid metabolic gene expression can be regulated by either xmrk, Myc, or both oncogenes. Our computational models showed that variations in total levels of several lipid classes are associated with HCC progression. Conclusions These data indicate that xmrk and Myc can temporally regulate lipid species that may serve as effective biomarkers of HCC progression. Supplementary Information The online version contains supplementary material available at 10.1186/s40170-022-00283-y.


Introduction
The adult liver can regulate lipid synthesis and degradation allowing it to play a major role in lipid metabolism [1]. Dysregulation of lipid production occurs in human hepatocellular carcinoma (HCC) [2,3], a form of liver cancer that is the third leading cause of cancer-related death in the world according to the World Health Organization (https:// www. who. int/ news-room/ fact-sheets/ detail/ cancer). Because of the societal impact of HCC, there is considerable interest in the development of new models that can act as analytical platforms for the study of this disease. The zebrafish model is readily adaptable for studying human cancer as the zebrafish genome exhibits a high degree of sequence conservation with human oncogenes, exhibits similar tumor development and physiology, and is readily adaptable to genetic and chemical screening [4,5]. Further, cancer cells expressed in zebrafish have very similar genetic and genomic characteristics relative to their human counterparts [6]. Recently, transgenic adult zebrafish models have been successfully used to study oncogenes, immune physiology, gender-based effects, hormonal signaling, metastasis, drug efficacy, and tumor progression and regression in HCC [7][8][9][10][11][12][13].
We recently generated a doxycycline (DOX)-inducible liver tumor zebrafish model that transgenically expresses the myelocytomatosis (Myc) and the Xiphophorus melanoma receptor tyrosine kinase (xmrk activated epidermal growth factor receptor (EGFR) homolog) oncogenes [10,11]. These models allowed us to control the temporality and location of oncogene induction via DOX exposure through regulation of the liver fatty acid-binding protein (fabp10a) promoter which is only expressed in hepatocytes [14]. Here, we used the xmrk and Myc transgenic fish models to identify potential lipid biomarkers of HCC progression. Histological analysis demonstrated that Myc fish developed hyperplasia, xmrk fish induced HCC stage I, and some xmrk/Myc fish acquired the characteristics of HCC stage II. Single and double transgenic fish had less lipid accumulation in the early stages of tumor progression but began to exhibit markedly increased lipid deposition in the later stages. This increased lipid accumulation is similar to conditions observed during nonalcoholic fatty liver disease (NAFLD), a spectrum of disorders that comprise nonalcoholic steatohepatitis, cirrhosis, and HCC [15], although specific lipid levels can be either decreased or increased in NAFLD [16].
Comparative lipidomic analysis of the different transgenic models revealed that the levels of most lipid species in cancer cells increased except for triglycerides. Examination of the lipid species regulation profiles suggests that Myc may drive certain changes in lipid levels at earlier time points and that xmrk may take over this role at some later stage before the onset of mortality. Analyses of the relative expression of lipogenic transcription factors, lipogenic enzymes, and lipid β-oxidation genes indicate that xmrk and Myc may regulate distinct metabolic genes and may even counteract each other's effects. Univariate and multifactorial modeling showed that several lipid classes increased during HCC progression, but that increased levels of other classes were correlated with restoration of the normal phenotype. Thus, xmrk and Myc signaling may act through specific lipids and lipid metabolic genes which may have utility as either diagnostic or prognostic biomarkers.

Zebrafish maintenance and transgene induction
Zebrafish embryos were raised as previously described [17]. Adult zebrafish were euthanized by adding 1× tricaine (Sigma, St. Louis, USA) to the maintenance solution. Generation of TO(xmrk) (x+m−); TO(Myc) (x−m+) and TO(xmrk/Myc) (x+m+) transgenic models under the control of the lfabp promoter has been described previously [8,11]. All zebrafish studies were approved by the Deakin University Animal Welfare Committee (AWC G17-2015) and the Institutional Animal Care and Use Committee of the National University of Singapore (Protocol 079/07). Transgenes were induced by exposing adult zebrafish (90 days old) to 40 μg/ml doxycycline (DOX) [10,11].

Histology sample preparation and analysis
Liver samples taken from 1.5, 3, 4.5, 6, and 7.5 days post-DOX treatment (dpt) fish were slowly dehydrated using a series of increasing ethanol solution concentrations (70%, 90%, 95%, and 100%). Specimens were then embedded in paraffin using a Leica EG1120. Sectioning at 5 μm was performed using a Reichert-Jung 2030 microtome. Hematoxylin (H) (Vector Laboratories, Burlingame, CA, #H3404) and Eosin (E; Sigma, #HT110232) were used for H and E staining. Oil-Red-O (Sigma) staining of zebrafish liver sections was performed as previously described [18]. Identification and classification of tumor types were based on previously established criteria [6].

Lipidomic analysis
To normalize samples, three to seven zebrafish livers were dissected and homogenized using a Pro200 homogenizer (Pro Scientific, Oxford, CT). Protein concentration was then quantified using a Pierce ™ BCA protein assay kit (Life Technologies, Carlsbad, CA). An aliquot of each sample normalized to 30 μg was used for lipid extraction. Samples and standards used in this analysis were as previously described [19]. Briefly, samples from 1.5, 3, 4.5, 6, and 7.5 dpt were lyophilized to remove all liquid. Samples were then reconstituted prior to extraction in 10 μl of water and 10 μl of the internal standard mix was added to each sample. Lipids were extracted by adding 200 μl chloroform/methanol (2:1) followed by sonication for 30 min before the supernatant was transferred to a 96-well plate and dried under vacuum in a SpeedVac Concentrator (Thermo Scientific, Waltham, MA). Samples were then reconstituted with 50 μl water-saturated butanol and 50 μl methanol with 10 mM ammonium formate, and analyzed by LC ESI-MS/MS, a technique that allows consistent measurement of various lipid species and classes in zebrafish over different experimental time points [20], using an Agilent 1200 LC system (Santa Clara, CA) and an AB Sciex 4000 Qtrap mass spectrometer (Darmstadt, Germany). Data were analyzed using MultiQuant 2.1 software (AB Sciex).

Quantitative real-time PCR analysis
Total RNA was extracted from 3, 4.5, and 6 dpt liver samples using the TRIzol ® (Invitrogen #15596-018) and chloroform method per the manufacturer's protocol. RNA was reverse-transcribed into cDNA using Transcriptor First Strand cDNA Synthesis Kit (Roche Applied Science, Penzberg, Germany). qRT-PCR was carried out using SYBR Green I Master Mix (Roche Applied Science) and a LightCycler ® 480 machine (Roche Life Science). Cycle numbers (C t ) of triplicate samples were averaged and then normalized to β-actin expression to obtain a ΔC t value. Final gene expression levels were calculated as 2 −ΔΔCt (see Supplemental Table 1 for a complete list of primers used in this study).

Lipid disease stage progression modeling analysis
As a final step, we constructed a model that would allow us to graphically depict the correlation between lipid levels and disease stage [21,22]. 24 univariate ordinal logistic regression models relating each lipid to disease stage, i.e., normal, hyperplasia/adenoma, HCC1, and HCC2 were constructed using data from all time points (1.5, 3, 4.5, 6, 7.5 dpt). To facilitate ease of interpretation, each lipid was scaled by dividing the value by roughly ¼ of its standard deviation. Then, we put all 24 logistic regression models into a multivariate fractional polynomial (MFP) ordinal logistic model with backward selection variable procedures enabled. This approach allowed us to construct a concise model where only random error and no systematic bias is present in lipid measurement and where additive effects that might propagate are accounted for by removing lipids that contribute to multicollinearity and only those total lipid classes that display independence in each HCC stage are considered [20][21][22]. All variables remained linear after the MFP procedure, with the exception of GM3, which was given a square root transformation.

Statistical analysis
Lipidomic samples were statistically evaluated (Graph-PAD PRISM, La Jolla, CA) using an ANOVA with Tukey's post hoc test. qRT-PCR samples were statistically evaluated using an ANOVA. All statistical analyses for the logistic modeling analysis were performed with Stata v16.1 (College Station, TX). A probability level of p < 0.05 was deemed significant throughout.

Rapid oncogenic transformation of hepatocytes into HCC cells in xmrk/Myc transgenic zebrafish
Three DOX-inducible zebrafish transgenic oncogene lines, xmrk (x+m−), Myc (x−m+), and xmrk/Myc (x+m+) were evaluated for their ability to transform hepatocytes into HCC cells. After 1.5 days post-treatment (dpt), all the transgenic groups (N = 10 per group) showed similar histology characteristics compared to the non-transgenic DOX-exposed control group (x−m−) ( Fig. 1), but at 3 dpt, single (x+m− and x−m+) and double transgenic zebrafish (x+m+) exhibited hyperplasia with increased cell number (Fig. 1B-D). Cellular transformation to HCC Grade I was first detected at 4.5 dpt in x+m− and x+m+ samples with evident increased apoptosis ( Fig. 1B, D, arrows), and multiple nuclei in one cell (Fig. 1D, black box). By 7.5 dpt, two x+m+ individuals showed grade II HCC phenotype, and x+m+ fish started to die after 7.5 dpt; therefore, 7.5 dpt was chosen as the end point for analysis. Unlike the other samples, x−m+ livers remained hyperplastic throughout all time points (Fig. 1C).
The LC ESI-MS/MS analysis also showed that inducing xmrk and Myc caused altered levels of numerous lipid species (Table 1, Table 1 for details). In x+m− samples, we found that xmrk induced regulation of different lipid species when compared to Myc transgenic samples. At 1.5 dpt, x+m− fish did not have decreased lipid species, but x+m− fish had increased levels of DHC 20:0, GM3 16:0, and multiple PC(O)s, PEs, PIs and TGs (Table 1). At 3 dpt, x+m− fish livers showed decreased levels of lipid species, e.g., the TGs, while only one PC(O), 35:4, was increased in x+m− samples (Table 1). Xmrk only transgenic samples at 4.5 dpt also showed increased PC(O) 35:4 and many other lipid species, e.g., several other PC(O)s, DHC 22:0, several PEs, PIs, and two PS species (Table 1). At 6 dpt, x+m− fish also showed increased PC(O) 20:0-0:0, but x+m− fish showed increased levels of DHCs, GM3 24:1, additional PC(O)s, PEs, PIs, and several PSs (Table 1). At 7.5 dpt, x+m− fish samples did not show decreased TGs, but did show increased levels of PC(O)s, PEs, PIs, and PSs, and xmrk induction alone increased DHCs (Table 1).
The double transgenics also had unique lipid species characteristics. At 1.5 dpt, we found that x+m+ fish also had lipid species decreased in x−m+ samples and PC(O) s, additional PEs, PI 38:4 and PS 38.4, while unlike x+m−, x+m+ had increased PI 34.1 and decreased PC(O) 36:4 and PE 36:3 (Table 1). Double transgenics at 3 dpt exhibited similar lipid species regulation to x−m+ samples; however, x+m+ fish also had several TGs increased (Table 1). At 4.5 dpt, x+m+ lipid species levels in liver tissues resembled those in x+m− samples, but not to  (Table 1). At 6 dpt, x+m+ liver profiles were similar to those observed in x+m−, but x+m+ samples had increased levels of several TGs (Table 1). Data from 7.5 dpt time points showed that double transgenics exhibited similar PC(O), PE, and PI lipid species upregulation   (Table 1). However, x+m+ fish did not have significant levels of PS species, but only they expressed GM3 species (Table 1).

Association of lipid class regulation with HCC disease progression
To identify lipid classes that may contribute to HCC disease progression, we related each lipid class to HCC disease stage using univariate ordinal logistic regression models. Univariate analysis showed that total DHC, GM3, PC(O), and PI levels were correlated with increased promotion of HCC II (Fig. 6A), while increasing total PS, PE, and TG levels were associated with the normal, non-HCC phenotype (Fig. 6B,   The results of the univariate ordinal logistic regression models were placed into a multivariate fractional polynomial analysis with backward selection variable procedures enabled to determine which lipid classes significantly modulated the HCC disease stage. All results were linear except that total GM3 used a square root nonlinear transformation (AIC =88.7 and R 2 = 0.55). OR, odds ratio; p < 0.05.

Discussion
Lipids and their metabolites can play roles as energy, signaling, and/or prognostic biomarkers in HCC [34,35], but oncogene-mediated regulation of specific lipids and related metabolomic genes during liver cancer progression have not been well studied. Therefore, we utilized transgenic zebrafish models of HCC expressing the liver cancer-inducing oncogenes, xmrk and Myc [8][9][10][11] to analyze lipidomic profiles and lipid metabolic genes at different HCC stages. Xmrk is a fish homolog of the human epidermal growth factor receptor (EGFR), a transmembrane receptor tyrosine kinase that regulates multiple signaling pathways controlling cell proliferation, migration, and inhibition [36]. EGFR plays a complex role in liver cancer where it is typically upregulated [37]. Some forms of Myc act as transcription factors that can modulate EGFR gene expression [38], and Myc amplification is correlated with HCC development and lipogenesis [39,40]. Further, EGFR stimulation can upregulate Myc expression in HCC [41]. We found that x+m−, x−m+, and x+m+ liver samples exhibited characteristics of HCC oncogenesis [6] within 7.5 dpt (Fig. 1) indicating that these oncogenes modulate phenotypic attributes of HCC. These data suggest that combined transgene induction may enhance disease progression perhaps via xmrk-mediated self-potentiation by a signaling loop integrating Myc.
The presence of increased lipid levels at later time points in the single and double transgenics (Fig. 2) suggests that xmrk and Myc might promote HCC oncogenesis via specific types of lipids, but surprisingly, few of the lipid classes that we detected were altered (Fig. 3,  supplementary Fig. 1). During HCC, lipid metabolites and lipid-related gene regulation can exhibit extensive up-and downregulation [42], and we observed altered regulation of some lipid classes, e.g., DHC, PC(O), GM3, PI, PE, and PS (Fig. 3A, B), where a general upward trend in levels was found beginning at 4.5 or 6 dpt, and TGs, where there is a general downward trend across all time points (Fig. 3B). The lipid classes PE and PI also can be dysregulated and have been correlated with HCC disease and its progression [43,44]. Further, the overall increasing relative levels of lipid classes across the time points (Fig. 4) may suggest ongoing lipid metabolic reprogramming which is observed in human HCC as well, where it has been associated with enhanced lipid synthesis [45]. In summary, altered lipid levels could represent a response to the different metabolic and/or signaling demands encountered during HCC progression.
Analysis of specific lipid species showed that induction of xmrk and Myc alone in HCC may regulate largely distinct sets of lipid species suggesting that each transgene may target lipid-based metabolism and signaling mechanisms (Table 1). In human HCC cell lines, the expression of EGFR and other genes has been correlated with lipid species regulation [46]. Further, the Myc oncogene can modulate some lipid species during HCC, particularly members of the PG class [40]. Recently, EGFR and Myc signals have been shown to be integrated into a signaling axis that modulates oncogenesis [41]. Therefore, expressing both oncogenes simultaneously may result in the utilization of different lipid species when compared to the single transgene models. Interestingly, when we compared the lipid species associated with the single transgenics with lipid species associated with the double transgenics at the same time points, we found that at 1.5 and 3 dpt, Myc appeared to drive lipid species levels in the double transgenics, but that from 4.5 to 6 dpt, xmrk may supplant this role in x+m+ HCC liver samples (Table 1). These results suggest that shifts in the metabolic or signaling demands associated with particular HCC disease stages may be responsible for the transposition of xmrk and Myc regulation of lipid species in x+m+. However, neither oncogene has a primary role in modulating lipid species regulation in x+m+ samples at 7.5 dpt, when mortality begins to appear (Table 1). Lipid-based mechanisms can regulate cell death mechanisms in cancer [47], and some lipid classes, e.g., GM3, which exhibits increased levels in late HCC stage x+m+ samples (Fig. 3A), can inhibit EGFR function [35] suggesting that the levels of some lipids in the transgenic models could be associated with late HCC stage cell death. Distinct xmrk and Myc temporal regulation of lipids could mean that these oncogenes modulate different lipid metabolic genes. Measuring the expression profiles of these genes over various time points allows assessment of their status as biomarkers without necessitating the analysis of the corresponding enzyme profiles or their mechanistic role in regulating lipid levels. The expression profiles for the lipogenic factors, pparg and srebf1, and for the enzyme, dgat2, in x+m+ samples are generally similar to those observed in xmrk only model (Fig. 5A), suggesting that xmrk may control these genes; although, Myc may be counteracting xmrk regulation of srebf1 by 6 dpt (Fig. 5A). Activation of pparg causes growth inhibition and cell death in HCC cells [23] suggesting that xmrk-mediated reduction of pparg expression (Fig. 5A) may promote oncogenesis in HCC. Increased expression of srebf1 is associated with cell proliferation in liver cancer [28] indicating that Myc may contribute to HCC oncogenesis at 6 dpt (Fig. 5A). As suppression of the lipogenic enzyme gene, dgat2, is associated with increased HCC cell proliferation [30], xmrk induction also could be driving disease progression by targeting dgat2 function (Fig. 5B). Expression of the lipid β-oxidation genes exhibited more complex regulation with pparab correlated with Myc, cpt1 with both xmrk and Myc, and cyp4a10 with xmrk (Fig. 5C). As reduced expression of pparab is associated with increased HCC development [27], Myc could be promoting liver cancer onset by suppressing lipid breakdown via pparab function. Further, as increased cpt1 is associated with HCC progression [31], these data support the suggestion that xmrk and Myc induction together promote the development of liver cancer via enhanced cpt1 expression. Interestingly, cyp4a10 expression is increased in liver tumor development in mice [26], but we found that cyp4a10 expression in the x+m+ samples was reduced suggesting that xmrk may counteract HCC progression through this gene (Fig. 5C). Therefore, xmrk and Myc may modulate distinct subsets of lipid metabolic genes that may have some utility as biomarkers of HCC progression.
In support of this concept, altered lipid levels may act as an indicator of HCC disease progression as lipid levels change during HCC [42,48]. DHC is associated with the regulation of differentiation, proliferation, and programmed cell death [49], and its elevated levels (Fig. 6A, Table 2) may be an indicator of oncogenic progression.
GM3 can suppress cancer progression [50]; therefore, its elevation during HCC progression (Fig. 6A, Table 2) could be a biomarker of a cellular response to counteract oncogenesis. PC(O) is a phospholipid, and some phospholipid metabolites can mediate proliferative growth and programmed cell death in cancer [51]; therefore, PC(O) elevation (Fig. 6A, Table 2) may be a marker of advanced HCC disease stage. PI species can participate in cell signaling in cancer [52] suggesting that increased PI levels during HCC (Fig. 6A, Table 2) could indicate either pro-or antioncogenic responses. PE phospholipids exhibit increased levels during cancer where they play a role in cell division and death [53]. Increased PE (Fig. 6B, Table 2) during HCC progression could represent activation of a cell death mechanism that counteracts liver cancer. PS phospholipid levels may regulate phagocytosis, immunosuppression, tumor growth, and metastasis [54]. Therefore, increased PS levels (Fig. 6B, Table 2) could indicate phagocytic anticancer activity. TG levels in the liver are regulated by uptake, secretory, and metabolic mechanisms [55], and higher TG levels (Fig. 6B, Table 2) could represent reduced mobilization of TG for driving mechanisms that promote HCC oncogenesis. Thus, altered levels of some lipid classes and their constituent species may act as diagnostic biomarkers of HCC disease progression.
When performing liver lipid analysis in HCC, it is important to bear in mind that changes in the diet can affect liver lipid content [56]. For this reason, our control and DOX-exposed fish were fed the same diet and amount to rule out inducing any effect from qualitative or quantitative variations in food intake. Moreover, in rats, dietary change can affect liver lipid content in nonalcoholic fatty liver disease (NAFLD) models, but the main lipid classes that displayed a strong variability in the liver were TGs and the lysophospholipids, LPC and LPE, which were higher in high-fat diet (HFD) rat livers [57]. This observation is consistent with our data as neither LPC nor LPE were identified as biomarkers of cancer progression ( Figure 6). Further, unlike in many cases of HFD, where TGs levels are dramatically increased in the liver [57][58][59][60], in the data we derived, decreased TG levels indicate a non-cancerous liver ( Figure 6B). Therefore, the lipid species identified in this study are not responding to diet variations and, instead, are hepatocyte cancer state specific. As zebrafish are lecithotrophic organisms, i.e., the embryo receives no nutrition other than what the yolk sac contains originally, a difference in the lipid content deposited in the yolk sac by the mother during oocyte formation might affect the future lipid content of the liver in adults. However, this effect is unlikely to have occurred in this study, because